Why Is a Linear Polynomial in [X] Always Irreducible?
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Rational Root Theorem and Synthetic Division Worksheet
Rational Root Theorem And Synthetic Division Worksheet Executory Geoffry approximate milkily or stodged tattily when Chaddie is unpastoral. Abaxial and Armenoid Garth nitrogenizes manneristically and gunges his Kaliyuga tremendously and availingly. Ivory-towered Wallas sometimes window his chechakoes recollectedly and ceres so underarm! In this section, we are discuss the variety of tools for writing polynomial functions and solving polynomial equations. This section we can find a quick foray into math help, use cookies to find all wikis and dirty test these theorems. Using synthetic division and rational root theorems. First look into factoring polynomials. How is my work scored? By the Factor Theorem, these zeros have factors associated with them. Rational Root Theorem Displaying top worksheets found for faith concept. Then determine the list of a synthetic division because if the! 23 Obj Students will that long division and synthetic division to divide polynomials. Solution because we can solve the original deed as follows. Work examples Homework: Pg. The resulting polynomial is now reduced to a quadratic equation so we can stop with the synthetic division and solve for the remaining zeros by either factoring or the quadratic formula. C Use the Rational Root Theorem to cellar the nostril of an possible rational roots it. For understanding the theorem and their uses cookies off or zero positive and rational root theorem and synthetic division worksheet if the. Very subtle but accuracy and synthetic division because if a root theorems and update to do not. Zero Theorem in party list further possible fractions can. End Encrypted Data After Losing Private Key? Rational Root Theorem Worksheet Kalmia. -
F[X] Be an Irreducible Cubic X 3 + Ax 2 + Bx + Cw
Math 404 Assignment 3. Due Friday, May 3, 2013. Cubic equations. Let f(x) F [x] be an irreducible cubic x3 + ax2 + bx + c with roots ∈ x1, x2, x3, and splitting field K/F . Since each element of the Galois group permutes the roots, G(K/F ) is a subgroup of S3, the group of permutations of the three roots, and [K : F ] = G(K/F ) divides (S3) = 6. Since [F (x1): F ] = 3, we see that [K : F ] equals ◦ ◦ 3 or 6, and G(K/F ) is either A3 or S3. If G(K/F ) = S3, then K has a unique subfield of dimension 2, namely, KA3 . We have seen that the determinant J of the Jacobian matrix of the partial derivatives of the system a = (x1 + x2 + x3) − b = x1x2 + x2x3 + x3x1 c = (x1x2x3) − equals (x1 x2)(x2 x3)(x1 x3). − − − Formula. J 2 = a2b2 4a3c 4b3 27c2 + 18abc F . − − − ∈ An odd permutation of the roots takes J =(x1 x2)(x2 x3)(x1 x3) to J and an even permutation of the roots takes J to J. − − − − 1. Let f(x) F [x] be an irreducible cubic polynomial. ∈ (a). Show that, if J is an element of K, then the Galois group G(L/K) is the alternating group A3. Solution. If J F , then every element of G(K/F ) fixes J, and G(K/F ) must be A3, ∈ (b). Show that, if J is not an element of F , then the splitting field K of f(x) F [x] has ∈ Galois group G(K/F ) isomorphic to S3. -
January 10, 2010 CHAPTER SIX IRREDUCIBILITY and FACTORIZATION §1. BASIC DIVISIBILITY THEORY the Set of Polynomials Over a Field
January 10, 2010 CHAPTER SIX IRREDUCIBILITY AND FACTORIZATION §1. BASIC DIVISIBILITY THEORY The set of polynomials over a field F is a ring, whose structure shares with the ring of integers many characteristics. A polynomials is irreducible iff it cannot be factored as a product of polynomials of strictly lower degree. Otherwise, the polynomial is reducible. Every linear polynomial is irreducible, and, when F = C, these are the only ones. When F = R, then the only other irreducibles are quadratics with negative discriminants. However, when F = Q, there are irreducible polynomials of arbitrary degree. As for the integers, we have a division algorithm, which in this case takes the form that, if f(x) and g(x) are two polynomials, then there is a quotient q(x) and a remainder r(x) whose degree is less than that of g(x) for which f(x) = q(x)g(x) + r(x) . The greatest common divisor of two polynomials f(x) and g(x) is a polynomial of maximum degree that divides both f(x) and g(x). It is determined up to multiplication by a constant, and every common divisor divides the greatest common divisor. These correspond to similar results for the integers and can be established in the same way. One can determine a greatest common divisor by the Euclidean algorithm, and by going through the equations in the algorithm backward arrive at the result that there are polynomials u(x) and v(x) for which gcd (f(x), g(x)) = u(x)f(x) + v(x)g(x) . -
Selecting Polynomials for the Function Field Sieve
Selecting polynomials for the Function Field Sieve Razvan Barbulescu Université de Lorraine, CNRS, INRIA, France [email protected] Abstract The Function Field Sieve algorithm is dedicated to computing discrete logarithms in a finite field Fqn , where q is a small prime power. The scope of this article is to select good polynomials for this algorithm by defining and measuring the size property and the so-called root and cancellation properties. In particular we present an algorithm for rapidly testing a large set of polynomials. Our study also explains the behaviour of inseparable polynomials, in particular we give an easy way to see that the algorithm encompass the Coppersmith algorithm as a particular case. 1 Introduction The Function Field Sieve (FFS) algorithm is dedicated to computing discrete logarithms in a finite field Fqn , where q is a small prime power. Introduced by Adleman in [Adl94] and inspired by the Number Field Sieve (NFS), the algorithm collects pairs of polynomials (a; b) 2 Fq[t] such that the norms of a − bx in two function fields are both smooth (the sieving stage), i.e having only irreducible divisors of small degree. It then solves a sparse linear system (the linear algebra stage), whose solutions, called virtual logarithms, allow to compute the discrete algorithm of any element during a final stage (individual logarithm stage). The choice of the defining polynomials f and g for the two function fields can be seen as a preliminary stage of the algorithm. It takes a small amount of time but it can greatly influence the sieving stage by slightly changing the probabilities of smoothness. -
Effective Noether Irreducibility Forms and Applications*
Appears in Journal of Computer and System Sciences, 50/2 pp. 274{295 (1995). Effective Noether Irreducibility Forms and Applications* Erich Kaltofen Department of Computer Science, Rensselaer Polytechnic Institute Troy, New York 12180-3590; Inter-Net: [email protected] Abstract. Using recent absolute irreducibility testing algorithms, we derive new irreducibility forms. These are integer polynomials in variables which are the generic coefficients of a multivariate polynomial of a given degree. A (multivariate) polynomial over a specific field is said to be absolutely irreducible if it is irreducible over the algebraic closure of its coefficient field. A specific polynomial of a certain degree is absolutely irreducible, if and only if all the corresponding irreducibility forms vanish when evaluated at the coefficients of the specific polynomial. Our forms have much smaller degrees and coefficients than the forms derived originally by Emmy Noether. We can also apply our estimates to derive more effective versions of irreducibility theorems by Ostrowski and Deuring, and of the Hilbert irreducibility theorem. We also give an effective estimate on the diameter of the neighborhood of an absolutely irreducible polynomial with respect to the coefficient space in which absolute irreducibility is preserved. Furthermore, we can apply the effective estimates to derive several factorization results in parallel computational complexity theory: we show how to compute arbitrary high precision approximations of the complex factors of a multivariate integral polynomial, and how to count the number of absolutely irreducible factors of a multivariate polynomial with coefficients in a rational function field, both in the complexity class . The factorization results also extend to the case where the coefficient field is a function field. -
Generation of Irreducible Polynomials from Trinomials Over GF(2). I
INFORMATION AND CONTROL 30, 396-'407 (1976) Generation of Irreducible Polynomials from Trinomials over GF(2). I B. G. BAJOGA AND rvV. J. WALBESSER Department of Electrical Engineering, Ahmadu Bello University, Zaria, Nigeria Methods of generating irreducible polynomials from a given minimal polynomial are known. However, when dealing with polynomials of large degrees many of these methods are laborious, and computers have to be used. In this paper the problem of generating irreducible polynomials from trinomials is investigated. An efficient technique of computing the minimum polynomial of c~k over GF(2) for certain values of k, when the minimum polynomial of c~ is of the form x m 4- x + 1, is developed, and explicit formulae are given. INTRODUCTION The generation of irreducible polynomials over GF(2) has been a subject of a number of investigations mainly because these polynomials are important not only in the study of linear sequencies but also in BCH coding and decoding. Many results have been obtained (Albert, 1966; Daykin, 1960). Computational methods for generating minimal polynomials from a given irreducible polynomial have been developed. These have been described by Berlekamp (1968), Golomb (1967), and Lempel (1971), among others. Berlekamp observed that all these methods are helpful for hand calculation only if the minimal polynomial from which others are generated is of low degree. He further pointed out that it proves easiest to compute polynomials of large degree by computer using the matrix method. In addition these methods use algorithms. Seldom do they provide results of a general nature. However, utilizing the underlying ideas of the matrix method, some general results on generating irreducible polynomials from a large class of trinomials are derived in this paper. -
Optimal Irreducible Polynomials for GF(2M) Arithmetic
Optimal Irreducible Polynomials for GF(2m) arithmetic Michael Scott School of Computing Dublin City University GF(2m) polynomial representation A polynomial with coefficients either 0 or 1 (m is a small prime) Stored as an array of bits, of length m, packed into computer words Addition (and subtraction) – easy – XOR. No reduction required as bit length does not increase. GF(2m) arithmetic 1 Squaring, easy, simply insert 0 between coefficients. Example 110101 → 10100010001 Multiplication – artificially hard as instruction sets do not support “binary polynomial” multiplication, or “multiplication without carries” – which is actually simpler in hardware than integer multiplication! Really annoying! GF(2m) arithmetic 2 So we use Comb or Karatsuba methods… Squaring or multiplication results in a polynomial with 2m-1 coefficients. This must be reduced with respect to an irreducible polynomial, to yield a field element of m bits. For example for m=17, x17+x5+1 GF(2m) arithmetic 3 This trinomial has no factors (irreducible) Reduction can be performed using shifts and XORs x17+x5+1 = 100000000000100001 Example – reduce 10100101010101101010101 GF(2m) arithmetic 4 10100101010101101010101 100000000000100001 ⊕ 00100101010110000110101 ← 100101010110000110101 100000000000100001 ⊕ 000101010110100111101 ← 101010110100111101 100000000000100001 ⊕ 001010110100011100 ← 1010110100011100 → result! Reduction in software - 1 Consider the standard pentanomial x163+x7+x6+x3+1 Assume value to be reduced is represented as 11 32-bit words g[.] To -
Tests and Constructions of Irreducible Polynomials Over Finite Fields
Tests and Constructions of Irreducible Polynomials over Finite Fields 1 2 Shuhong Gao and Daniel Panario 1 Department of Mathematical Sciences, Clemson University, Clemson, South Carolina 29634-1907, USA E-mail: [email protected] 2 Department of Computer Science, UniversityofToronto, Toronto, Canada M5S-1A4 E-mail: [email protected] Abstract. In this pap er we fo cus on tests and constructions of irre- ducible p olynomials over nite elds. We revisit Rabin's 1980 algo- rithm providing a variant of it that improves Rabin's cost estimate by a log n factor. We give a precise analysis of the probability that a ran- dom p olynomial of degree n contains no irreducible factors of degree less than O log n. This probability is naturally related to Ben-Or's 1981 algorithm for testing irreducibility of p olynomials over nite elds. We also compute the probability of a p olynomial b eing irreducible when it has no irreducible factors of low degree. This probability is useful in the analysis of various algorithms for factoring p olynomials over nite elds. We present an exp erimental comparison of these irreducibility metho ds when testing random p olynomials. 1 Motivation and results For a prime power q and an integer n 2, let IF be a nite eld with q q n elements, and IF be its extension of degree n. Extensions of nite elds are q imp ortant in implementing cryptosystems and error correcting co des. One way of constructing extensions of nite elds is via an irreducible p olynomial over the ground eld with degree equal to the degree of the extension. -
Finding Roots of Polynomials SHOW ALL WORK and JUSTIFY ALL ANSWERS
Name: Math 1314 College Algebra Finding Roots of Polynomials SHOW ALL WORK AND JUSTIFY ALL ANSWERS. Find all zeroes of P(x) = 2x6 − 3x5 − 13x4 + 29x3 − 27x2 + 32x − 12. 1. Make sure all terms of P(x) are written in descending order. 2. Can we factor out a greatest common factor from each term? 3. Is 0 a zero? 0 is a zero if P(0) = 0: 4. Look for sign changes in P(x): P(x) = 2x6 − 3x5 − 13x4 + 29x3 − 27x2 + 32x − 12 There are sign changes in P(x). Summary of Descartes’ Rule of Signs: For potential positive real zeroes: Count sign changes in P(x). Take that number and continue to subtract 2 until you get 0 or 1: The original value and each result of the subtraction could be the number of positive real zeroes. Using Descartes’ Rule of Signs, we could have or or positive real zeroes. 5. Find P(−x) To find P(−x), change signs of all coefficients of xodd power P(−x) = There are sign changes in P(−x). Summary of Descartes’ Rule of Signs: For potential negative real zeroes: Count sign changes in P(−x). Take that number and continue to subtract 2 until you get 0 or 1: The original value and each result of the subtraction could be the number of negative real zeroes. Using Descartes’ Rule of Signs, we will have negative real zero. 6. Fill in the values for possible zeroes. I have started for you. Remember that every row must add up to the degree of the polynomial, which is 6 in this example. -
Section V.4. the Galois Group of a Polynomial (Supplement)
V.4. The Galois Group of a Polynomial (Supplement) 1 Section V.4. The Galois Group of a Polynomial (Supplement) Note. In this supplement to the Section V.4 notes, we present the results from Corollary V.4.3 to Proposition V.4.11 and some examples which use these results. These results are rather specialized in that they will allow us to classify the Ga- lois group of a 2nd degree polynomial (Corollary V.4.3), a 3rd degree polynomial (Corollary V.4.7), and a 4th degree polynomial (Proposition V.4.11). In the event that we are low on time, we will only cover the main notes and skip this supple- ment. However, most of the exercises from this section require this supplemental material. Note. The results in this supplement deal primarily with polynomials all of whose roots are distinct in some splitting field (and so the irreducible factors of these polynomials are separable [by Definition V.3.10, only irreducible polynomials are separable]). By Theorem V.3.11, the splitting field F of such a polynomial f K[x] ∈ is Galois over K. In Exercise V.4.1 it is shown that if the Galois group of such polynomials in K[x] can be calculated, then it is possible to calculate the Galois group of an arbitrary polynomial in K[x]. Note. As shown in Theorem V.4.2, the Galois group G of f K[x] is iso- ∈ morphic to a subgroup of some symmetric group Sn (where G = AutKF for F = K(u1, u2,...,un) where the roots of f are u1, u2,...,un). -
POLYNOMIALS Gabriel D
POLYNOMIALS Gabriel D. Carroll, 11/14/99 The theory of polynomials is an extremely broad and far-reaching area of study, having applications not only to algebra but also ranging from combinatorics to geometry to analysis. Consequently, this exposition can only give a small taste of a few facets of this theory. However, it is hoped that this will spur the reader’s interest in the subject. 1 Definitions and basic operations First, we’d better know what we’re talking about. A polynomial in the indeterminate x is a formal expression of the form n n n 1 i f(x) = cnx + cn 1x − + + c1x + x0 = cix − ··· X i=0 for some coefficients ci. Admittedly this definition is not quite all there in that it doesn’t say what the coefficients are. Generally, we take our coefficients in some field. A field is a system of objects with two operations (generally called “addition” and “multiplication”), both of which are commutative and associative, have identities, and are related by the distributive law; also, every element has an additive inverse, and everything except the additive identity has a multiplicative inverse. Examples of familiar fields are the rational numbers Q, the reals R, and the complex numbers C, though plenty of other examples exist, both finite and infinite. We let F [x] denote the set of all polynomials “over” (with coefficients in) the field F . Unless otherwise stated, don’t worry about what field we’re working over. n i A few more terms should be defined before we proceed. First, if f(x) = i=0 cix with cn = 0, we say n is the degree of the polynomial, written deg f. -
Examples of Proofs by Induction
EXAMPLES OF PROOFS BY INDUCTION KEITH CONRAD 1. Introduction Mathematical induction is a method that allows us to prove infinitely many similar assertions in a systematic way, by organizing the results in a definite order and showing • the first assertion is correct (\base case") • whenever an assertion in the list is correct (\inductive hypothesis"), prove the next assertion in the list is correct (\inductive step"). This tells us every assertion in the list is correct, which is analogous to falling dominos. If dominos are close enough and each domino falling makes the next domino fall, then after the first domino falls all the dominos will fall. The most basic results that are proved by induction are summation identities, such as n(n + 1) (1.1) 1 + 2 + 3 + ··· + n = 2 for all positive integers n. Think about this identity as a separate statement for each n: S(1);S(2);S(3); and so on, where S(n) is the (as yet unproved) assertion in (1.1). Definitely S(1) is true, since the left side of (1.1) when n = 1 is 1 and the right side of (1.1) when n = 1 is 1(1 + 1)=2 = 1. Next, if S(n) is true for some n, then we can show S(n + 1) is true by writing 1 + 2 + ··· + n + (n + 1) in terms of 1 + 2 + ··· + n and using the truth of S(n): 1 + 2 + ··· + n + (n + 1) = (1 + 2 + ··· + n) + (n + 1) n(n + 1) = + (n + 1) since S(n) is assumed to be true 2 n(n + 1) + 2(n + 1) = 2 (n + 1)(n + 2) = : 2 The equality of the first and last expressions here is precisely what it means for S(n + 1) to be true.